The existing approaches share a common philosophy in that windstorms are modelled in an Eulerian frame of reference. In fluid mechanics, this approach refers to the scenario whereby an observer measures observations of a process at a fixed location while the process, e.g., a windstorm, passes over. So, it is a Eulerian framework that is being adopted when analysing data collected at a fixed set of sites over time, such as when modelling weather station data. It is the standard approach for statistical modelling of spatial data [1]}, with the advantage that one can build large data sets, with time series at each location being observed. However, if one's concern is focused more on modelling the evolution and influence of the process itself, a Lagrangian frame of reference is required. This approach to modelling requires following the process and collecting observations as it moves through space and time. We take this approach as it is a natural framework on which to build a model for windstorms, since it allows us to model the behaviour of extreme winds relative to the storm centre.
